Human ovarian cancer intrinsic mechanisms regulate lymphocyte activation in response to immune checkpoint blockade

Loss of NEDD8 in cancer cells causes vulnerability to immune checkpoint blockade in triple-negative breast cancer

Immune checkpoint blockade therapy aims to activate the immune system to eliminate cancer cells. However, clinical benefits are only recorded in a subset of patients. Here, we leverage genome-wide CRISPR/Cas9 screens in a Tumor-Immune co-Culture System focusing on triple-negative breast cancer (TNBC). We reveal that NEDD8 loss in cancer cells causes a vulnerability to nivolumab (anti-PD-1). Genetic deletion of NEDD8 only delays cell division initially but cell proliferation is unaffected after recovery. Since the NEDD8 gene is commonly essential, we validate this observation with additional CRISPR screens and uncover enhanced immunogenicity in NEDD8 deficient cells using proteomics. In female immunocompetent mice, PD-1 blockade lacks efficacy against established EO771 breast cancer tumors. In contrast, we observe tumor regression mediated by CD8+ T cells against Nedd8 deficient EO771 tumors after PD-1 blockade. In essence, we provide evidence that NEDD8 is conditionally essential in TNBC and presents as a synergistic drug target for PD-1/L1 blockade therapy.

Comprehensive analysis of the interaction of antigen presentation during anti-tumour immunity and establishment of AIDPS systems in ovarian cancer

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation-associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival-SVM). Then, in the train cohort, the prediction model was fitted using a leave-one cross-validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C-index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C-index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning-based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C-index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C-index and the highest C-index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C-index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen-presenting anti-tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen-presenting model obtained by the RSF + Lasso method has the best C-INDEX, which plays a key role in developing antigen-presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients.

Strategies to synergize PD-1/PD-L1 targeted cancer immunotherapies to enhance antitumor responses in ovarian cancer

Anti-programmed cell death 1/programmed cell death ligand 1 (anti-PD-1/PD-L1) antibodies have developed rapidly but exhibited modest activity in ovarian cancer (OC), achieving a clinical response rate ranging from 5.9% to 19%. Current evidence indicate that the establishment of an integrated cancer-immunity cycle is a prerequisite for anti-PD-1/PD-L1 antibodies. Any impairment in this cycle, including lack of cancer antigens release, impaired antigen-presenting, decreased T cell priming and activation, less T cells that are trafficked or infiltrated in tumor microenvironment (TME), and low tumor recognition and killings, will lead to decreased infiltrated cytotoxic T cells to tumor bed and treatment failure. Therefore, combinatorial strategies aiming to modify cancer-immunity cycle and reprogram tumor immune microenvironment are of great interest. By far, various strategies have been studied to enhance responsiveness to PD-1/PD-L1 inhibitors in OC. Platinum-based chemotherapy increases neoantigens release; poly (ADP-ribose) polymerase (PARP) inhibitors (PARPis) improve the function of antigen-presenting cells and promote the trafficking of T cells into tumors; epigenetic drugs help to complete the immune cycle by affecting multiple steps; immunotherapies like anti-cytotoxic T lymphocyte antigen 4 (CTLA-4) antibodies reactivate T cells, and other treatment strategies like radiotherapy helps to increase the expression of tumor antigens. In this review, we will summarize the preclinical studies by analyzing their contribution in modifying the cancer immunity cycle and remodeling tumor environment, and we will also summarize recent progress in clinical trials and discuss some perspectives to improve these treatment strategies.

Copenhagen index (CPH-I) is more favorable than CA125, HE4, and risk of ovarian malignancy algorithm (ROMA): Nomogram prediction models with clinical-ultrasonographic feature for diagnosing ovarian neoplasms

Background

We aimed to analyze the benign and malignant identification efficiency of CA125, HE4, risk of ovarian malignancy algorithm (ROMA), Copenhagen Index (CPH-I) in ovarian neoplasms and establish a nomogram to improve the preoperative evaluation value of ovarian neoplasms.

Methods

A total of 3,042 patients with ovarian neoplasms were retrospectively classified according to postoperative pathological diagnosis [benign, n = 2389; epithelial ovarian cancer (EOC), n = 653]. The patients were randomly divided into training and test cohorts at a ratio of 7:3. Using CA125, HE4, ROMA, and CPH-I, Receiver operating characteristic (ROC) curves corresponding to different truncation values were calculated and compared, and optimal truncation values were selected. Clinical and imaging risk factors were calculated using univariate regression, and significant variables were selected for multivariate regression analysis combined with ROMA and CPH-I. Nomograms were constructed to predict the occurrence of EOC, and the accuracy was assessed by external validation.

Results

When the cutoff value of CA125, HE4, ROMA, and CPH-I was 100 U/ml, 70 pmol/L, 12.5/14.4% (premenopausal/postmenopausal) and 5%, respectively, the AUC was 0.674, 0.721, 0.750 and 0.769, respectively. From univariate regression, the clinical risk factors were older age, menopausal status, higher birth rate, hypertension, and diabetes; imaging risk factors were multilocular tumors, solid nodules, bilateral tumors, larger tumor diameter, and ascites. The AUC of the nomogram containing ROMA and CPH-I was 0.8914 and 0.9114, respectively, which was better than the prediction accuracies of CA125, HE4, ROMA, and CPH-I alone. The nomogram with CPH-I was significantly better than that with ROMA (P < 0.001), and a nomogram decision curve analysis (DCA) containing CPH-I seemed to have better clinical benefits than ROMA. For external validation of this nomogram containing ROMA and CPH-I, the C-indices were 0.889 and 0.900, and the calibration curves were close to 45°, showing good agreement with the predicted values.

Conclusion

We conclude that CPH-I and ROMA have higher diagnostic values in the preoperative diagnosis of EOC than other single tumor markers like CA125 or HE4. A nomogram based on CPH-I and ROMA with clinical and ultrasonic indicators had a better diagnostic value, and the CPH-I nomogram had the highest diagnostic efficacy.

The challenge of selecting tumor antigens for chimeric antigen receptor T-cell therapy in ovarian cancer

Ovarian cancer (OC) is one of the most common cancers in women, with a high mortality rate and very few available and effective treatments. Evidence shows that immunotherapy in OC has not been very successful because immune checkpoint blockers have not achieved satisfactory clinical outcomes. On the other hand, as one of the effective treatment approaches, chimeric antigen receptor T-cell (CAR T-cell) therapy has gained a moral position, especially in blood malignancies. Although in solid tumors, CAR T-cell therapy faces various complications and challenges. One of these challenges is selecting the appropriate tumor antigen targeted by CAR T cells, making the selection difficult due to the expression of antigens by tumor cells and normal cells. In addition, the rate of tumor antigen expression and CAR T-cell access to the desired antigen and proper stimulation of CAR T cells can be other important points in antigen selection. This review summarized common tumor antigens and the challenges of selecting them in CAR T cells therapy of OC.

NK cells with tissue-resident traits shape response to immunotherapy by inducing adaptive antitumor immunity

T cell–directed cancer immunotherapy often fails to generate lasting tumor control. Harnessing additional effectors of the immune response against tumors may strengthen the clinical benefit of immunotherapies. Here, we demonstrate that therapeutic targeting of the interferon-γ (IFN-γ)–interleukin-12 (IL-12) pathway relies on the ability of a population of natural killer (NK) cells with tissue-resident traits to orchestrate an antitumor microenvironment. In particular, we used an engineered adenoviral platform as a tool for intratumoral IL-12 immunotherapy (AdV5–IL-12) to generate adaptive antitumor immunity. Mechanistically, we demonstrate that AdV5–IL-12 is capable of inducing the expression of CC-chemokine ligand 5 (CCL5) in CD49a + NK cells both in tumor mouse models and tumor specimens from patients with cancer. AdV5–IL-12 imposed CCL5-induced type I conventional dendritic cell (cDC1) infiltration and thus increased DC-CD8 T cell interactions. A similar observation was made for other IFN-γ–inducing therapies such as Programmed cell death 1 (PD-1) blockade. Conversely, failure to respond to IL-12 and PD-1 blockade in tumor models with low CD49a + CXCR6 + NK cell infiltration could be overcome by intratumoral delivery of CCL5. Thus, therapeutic efficacy depends on the abundance of NK cells with tissue-resident traits and, specifically, their capacity to produce the DC chemoattractant CCL5. Our findings reveal a barrier for T cell–focused therapies and offer mechanistic insights into how T cell–NK cell–DC cross-talk can be enhanced to promote antitumor immunity and overcome resistance.

Immune Subtypes Characterization Identifies Clinical Prognosis, Tumor Microenvironment Infiltration, and Immune Response in Ovarian Cancer

Objective: Because of the modest immunotherapeutic response among ovarian carcinoma (OC) patients, it is significant to evaluate antitumor immune response and develop more effective precision immunotherapeutic regimens. Here, this study aimed to determine diverse immune subtypes of OC.

Methods: This study curated the expression profiles of prognostic immunologically relevant genes and conducted consensus clustering analyses for determining immune subtypes among OC patients in TCGA cohort. With Boruta algorithm, characteristic genes were screened for conducting an immune scoring system through principal component analysis algorithm. The single-sample gene set enrichment analysis and ESTIAMTE methods were adopted for quantifying the immune infiltrates and responses to chemotherapeutic agents were estimated with pRRophetic algorithm. Two immunotherapeutic cohorts were used for investigating the efficacy of immune score in predicting therapeutic benefits.

Results: Two immune subtypes were conducted among 377 OC patients. Immune subtype 2 was characterized by worse clinical prognosis, more frequent genetic variations and mutations, enhanced immune infiltrates, and increased expression of MHC molecules and programmed cell death protein 1/programmed death ligand 1 (PD-1/PD-L1). In total, 30 prognosis-relevant characteristic immune subtype–derived genes were identified for constructing the immune score of OC patients. High immune score was linked with more dismal prognosis, decreased immune infiltrations, and expression of MHC molecules. High immune score presented favorable sensitivity to doxorubicin and vinorelbine and reduced sensitivity to cisplatin. In addition, immune score possessed the potential in predicting benefits from anti–PD-1/PD-L1 therapy.

Conclusion: Collectively, our findings propose two complex and diverse immune subtypes of OC. Quantitative assessment of immune subtypes in individual patients strengthens the understanding of tumor microenvironment features and promotes more effective immunotherapeutic regimens.

Assessing Preclinical Research Models for Immunotherapy for Gynecologic Malignancies

Gynecologic malignancies are increasing in incidence, with a plateau in clinical outcomes necessitating novel treatment options. Immunotherapy and modulation of the tumor microenvironment are rapidly developing fields of interest in gynecologic oncology translational research; examples include the PD-1 (programmed cell death 1) and CTLA-4 (cytotoxic T-lymphocyte-associated protein 4) axes and the Wnt pathway. However, clinical successes with these agents have been modest and lag behind immunotherapy successes in other malignancies. A thorough contextualization of preclinical models utilized in gynecologic oncology immunotherapy research is necessary in order to effectively and efficiently develop translational medicine. These include murine models, in vitro assays, and three-dimensional human-tissue-based systems. Here, we provide a comprehensive review of preclinical models for immunotherapy in gynecologic malignancies, including benefits and limitations of each, in order to inform study design and translational research models. Improved model design and implementation will optimize preclinical research efficiency and increase the translational value to positive findings, facilitating novel treatments that improve patient outcomes.

Transcriptional analysis of multiple ovarian cancer cohorts reveals prognostic and immunomodulatory consequences of ERV expression

BACKGROUND

Endogenous retroviruses (ERVs) play a role in a variety of biological processes, including embryogenesis and cancer. DNA methyltransferase inhibitors (DNMTi)-induced ERV expression triggers interferon responses in ovarian cancer cells via the viral sensing machinery. Baseline expression of ERVs also occurs in cancer cells, though this process is poorly understood and previously unexplored in epithelial ovarian cancer (EOC). Here, the prognostic and immunomodulatory consequences of baseline ERV expression was assessed in EOC.

METHODS

ERV expression was assessed using EOC transcriptional data from The Cancer Genome Atlas (TCGA) and from an independent cohort (Hammersmith Hospital, HH), as well as from untreated or DNMTi-treated EOC cell lines. Least absolute shrinkage and selection operator (LASSO) logistic regression defined an ERV expression score to predict patient prognosis. Immunohistochemistry (IHC) was conducted on the HH cohort. Combination of DNMTi treatment with γδ T cells was tested in vitro, using EOC cell lines and patient-derived tumor cells.

RESULTS

ERV expression was found to define clinically relevant subsets of EOC patients. An ERV prognostic score was successfully generated in TCGA and validated in the independent cohort. In EOC patients from this cohort, a high ERV score was associated with better survival (log-rank p=0.0009) and correlated with infiltration of CD8+PD1+T cells (r=0.46, p=0.0001). In the TCGA dataset, a higher ERV score was found in BRCA1/2 mutant tumors, compared to wild type (p=0.015), while a lower ERV score was found in CCNE1 amplified tumors, compared to wild type (p=0.019). In vitro, baseline ERV expression dictates the level of ERV induction in response to DNMTi. Manipulation of an ERV expression threshold by DNMTi resulted in improved EOC cell killing by cytotoxic immune cells.

CONCLUSIONS

These findings uncover the potential for baseline ERV expression to robustly inform EOC patient prognosis, influence tumor immune infiltration and affect antitumor immunity.

The Extrinsic and Intrinsic Roles of PD-L1 and Its Receptor PD-1: Implications for Immunotherapy Treatment

Programmed death-ligand 1 (PD-L1) is an immune checkpoint inhibitor that binds to its receptor PD-1 expressed by T cells and other immune cells to regulate immune responses; ultimately preventing exacerbated activation and autoimmunity. Many tumors exploit this mechanism by overexpressing PD-L1 which often correlates with poor prognosis. Some tumors have also recently been shown to express PD-1. On tumors, PD-L1 binding to PD-1 on immune cells promotes immune evasion and tumor progression, primarily by inhibition of cytotoxic T lymphocyte effector function. PD-1/PD-L1-targeted therapy has revolutionized the cancer therapy landscape and has become the first-line treatment for some cancers, due to their ability to promote durable anti-tumor immune responses in select patients with advanced cancers. Despite this clinical success, some patients have shown to be unresponsive, hyperprogressive or develop resistance to PD-1/PD-L1-targeted therapy. The exact mechanisms for this are still unclear. This review will discuss the current status of PD-1/PD-L1-targeted therapy, oncogenic expression of PD-L1, the new and emerging tumor-intrinisic roles of PD-L1 and its receptor PD-1 and how they may contribute to tumor progression and immunotherapy responses as shown in different oncology models.

The Perfect Combination: Enhancing Patient Response to PD-1-Based Therapies in Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy, with an overall 5-year survival of only 47%. As the development of novel targeted therapies is drastically necessary in order to improve patient survival, current EOC clinical trials have heavily focused on immunotherapeutic approaches, centered upon programmed cell death 1 (PD-1) inhibitors. While PD-1 monotherapies have only exhibited modest responses for patients, it has been theorized that in order to enhance EOC patient response to immunotherapy, combinatorial regimens must be investigated. In this review, unique challenges to EOC PD-1 response will be discussed, along with a comprehensive description of both preclinical and clinical studies evaluating PD-1-based combinatorial therapies. Promising aspects of PD-1-based combinatorial approaches are highlighted, while also discussing specific preclinical and clinical areas of research that need to be addressed, in order to optimize EOC patient immunotherapy response.